import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel






class   __lowercase       :

						def __init__(						self					:   Tuple				,				__lowerCamelCase					:   int				,				__lowerCamelCase					:   List[Any]=99				,				__lowerCamelCase					:   Any=13				,				__lowerCamelCase					:   str=7				,				__lowerCamelCase					:   int=9				,				__lowerCamelCase					:   List[str]=True				,				__lowerCamelCase					:   Optional[Any]=True				,				__lowerCamelCase					:   Union[str, Any]=False				,				__lowerCamelCase					:   Any=32				,				__lowerCamelCase					:   Any=5				,				__lowerCamelCase					:   Tuple=4				,				__lowerCamelCase					:   List[str]=37				,				__lowerCamelCase					:   Any=8				,				__lowerCamelCase					:   int=0.1				,				__lowerCamelCase					:   str=0.002				,				__lowerCamelCase					:   Optional[int]=1				,				__lowerCamelCase					:   int=0				,				__lowerCamelCase					:   Dict=0				,				__lowerCamelCase					:   Tuple=None				,				__lowerCamelCase					:   Optional[int]=None				,				)				->		List[str]:


									'''simple docstring'''

									lowercase       		=		parent
									lowercase       		=		batch_size
									lowercase       		=		encoder_seq_length
									lowercase       		=		decoder_seq_length
									# For common tests
									lowercase       		=		self.decoder_seq_length
									lowercase       		=		is_training
									lowercase       		=		use_attention_mask
									lowercase       		=		use_labels
									lowercase       		=		vocab_size
									lowercase       		=		hidden_size
									lowercase       		=		num_hidden_layers
									lowercase       		=		num_attention_heads
									lowercase       		=		d_ff
									lowercase       		=		relative_attention_num_buckets
									lowercase       		=		dropout_rate
									lowercase       		=		initializer_factor
									lowercase       		=		eos_token_id
									lowercase       		=		pad_token_id
									lowercase       		=		decoder_start_token_id
									lowercase       		=		None
									lowercase       		=		decoder_layers

						def 							__a		(						self					:   Optional[int] )				->		int:


									'''simple docstring'''

									return TaConfig.from_pretrained('''google/umt5-base''' )

						def 							__a		(						self					:   str				,				__lowerCamelCase					:   Optional[int]				,				__lowerCamelCase					:   Any				,				__lowerCamelCase					:   Optional[int]				,				__lowerCamelCase					:   Union[str, Any]=None				,				__lowerCamelCase					:   List[Any]=None				,				__lowerCamelCase					:   Dict=None				,				__lowerCamelCase					:   str=None				,				__lowerCamelCase					:   Optional[int]=None				,				)				->		int:


									'''simple docstring'''

									if attention_mask is None:
												lowercase       		=		input_ids.ne(config.pad_token_id )
									if decoder_attention_mask is None:
												lowercase       		=		decoder_input_ids.ne(config.pad_token_id )
									if head_mask is None:
												lowercase       		=		torch.ones(config.num_hidden_layers				,				config.num_attention_heads				,				device=a_ )
									if decoder_head_mask is None:
												lowercase       		=		torch.ones(config.num_decoder_layers				,				config.num_attention_heads				,				device=a_ )
									if cross_attn_head_mask is None:
												lowercase       		=		torch.ones(
												    config.num_decoder_layers				,				config.num_attention_heads				,				device=a_ )
									return {
									    "input_ids": input_ids,
									    "decoder_input_ids": decoder_input_ids,
									    "attention_mask": attention_mask,
									    "decoder_attention_mask": decoder_attention_mask,
									    "head_mask": head_mask,
									    "decoder_head_mask": decoder_head_mask,
									    "cross_attn_head_mask": cross_attn_head_mask,
									}

						def 							__a		(						self					:   Union[str, Any] )				->		Tuple:


									'''simple docstring'''

									lowercase       		=		ids_tensor([self.batch_size, self.encoder_seq_length]				,				self.vocab_size )
									lowercase       		=		ids_tensor([self.batch_size, self.decoder_seq_length]				,				self.vocab_size )

									# we need to clamp the input ids here to avoid having pad token in between
									# this is because for NllbMoe the position_ids are prepared such that
									# all pad tokens have pos id = 2 and rest are between 2..seq_length
									# and the seq_length here is seq_length - num_pad_tokens
									# but when using past, there is no way of knowing if the past input ids had
									# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
									# position_ids being off by num_pad_tokens in past input
									lowercase       		=		input_ids.clamp(self.pad_token_id + 1 )
									lowercase       		=		decoder_input_ids.clamp(self.pad_token_id + 1 )

									lowercase       		=		self.get_config()
									lowercase       		=		config.num_attention_heads
									lowercase       		=		self.prepare_inputs_dict(a_				,				a_				,				a_ )
									return config, input_dict

						def 							__a		(						self					:   List[Any] )				->		Optional[Any]:


									'''simple docstring'''

									lowercase       		=		self.prepare_config_and_inputs()
									return config, inputs_dict

						def 							__a		(						self					:   Tuple )				->		Dict:


									'''simple docstring'''

									return TaConfig(
									    vocab_size=1_66				,				d_model=self.hidden_size				,				d_ff=self.d_ff				,				d_kv=self.hidden_size // self.num_attention_heads				,				num_layers=self.num_hidden_layers				,				num_decoder_layers=self.decoder_layers				,				num_heads=self.num_attention_heads				,				relative_attention_num_buckets=self.relative_attention_num_buckets				,				dropout_rate=self.dropout_rate				,				initializer_factor=self.initializer_factor				,				eos_token_id=self.eos_token_id				,				bos_token_id=self.pad_token_id				,				pad_token_id=self.pad_token_id				,				decoder_start_token_id=self.decoder_start_token_id				,				)

						def 							__a		(						self					:   Union[str, Any] )				->		Any:


									'''simple docstring'''

									return TaConfig(
									    vocab_size=self.vocab_size				,				d_model=self.hidden_size				,				d_ff=self.d_ff				,				d_kv=self.hidden_size // self.num_attention_heads				,				num_layers=self.num_hidden_layers				,				num_decoder_layers=self.decoder_layers				,				num_heads=self.num_attention_heads				,				relative_attention_num_buckets=self.relative_attention_num_buckets				,				dropout_rate=self.dropout_rate				,				initializer_factor=self.initializer_factor				,				eos_token_id=self.eos_token_id				,				bos_token_id=self.pad_token_id				,				pad_token_id=self.pad_token_id				,				decoder_start_token_id=self.decoder_start_token_id				,				)

						def 							__a		(						self					:   Optional[int]				,				__lowerCamelCase					:   Union[str, Any]				,				__lowerCamelCase					:   Union[str, Any]				,				__lowerCamelCase					:   Optional[int]				,				__lowerCamelCase					:   int				,				__lowerCamelCase					:   Union[str, Any]				,				__lowerCamelCase					:   int				,				)				->		List[str]:


									'''simple docstring'''

									lowercase       		=		UMTaModel(config=a_ )
									model.to(a_ )
									model.eval()
									lowercase       		=		model(
									    input_ids=a_				,				decoder_input_ids=a_				,				attention_mask=a_				,				decoder_attention_mask=a_				,				)
									lowercase       		=		model(input_ids=a_				,				decoder_input_ids=a_ )
									lowercase       		=		result.last_hidden_state
									lowercase       		=		result.past_key_values
									lowercase       		=		result.encoder_last_hidden_state

									self.parent.assertEqual(encoder_output.size()				,				(self.batch_size, self.encoder_seq_length, self.hidden_size) )
									self.parent.assertEqual(decoder_output.size()				,				(self.batch_size, self.decoder_seq_length, self.hidden_size) )
									# There should be `num_layers` key value embeddings stored in decoder_past
									self.parent.assertEqual(len(a_ )				,				config.num_layers )
									# There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
									self.parent.assertEqual(len(decoder_past[0] )				,				4 )

						def 							__a		(						self					:   List[str]				,				__lowerCamelCase					:   Any				,				__lowerCamelCase					:   Union[str, Any]				,				__lowerCamelCase					:   Tuple				,				__lowerCamelCase					:   int				,				__lowerCamelCase					:   str				,				__lowerCamelCase					:   List[str]				,				)				->		Union[str, Any]:


									'''simple docstring'''

									lowercase       		=		UMTaModel(config=a_ ).get_decoder().to(a_ ).eval()
									# first forward pass
									lowercase       		=		model(a_				,				use_cache=a_ )
									lowercase       		=		model(a_ )
									lowercase       		=		model(a_				,				use_cache=a_ )

									self.parent.assertTrue(len(a_ ) == len(a_ ) )
									self.parent.assertTrue(len(a_ ) == len(a_ ) + 1 )

									lowercase       		=		outputs.to_tuple()

									# create hypothetical next token and extent to next_input_ids
									lowercase       		=		ids_tensor((self.batch_size, 1)				,				config.vocab_size )

									# append to next input_ids and
									lowercase       		=		torch.cat([input_ids, next_tokens]				,				dim=-1 )

									lowercase       		=		model(a_ )["last_hidden_state"]
									lowercase       		=		model(a_				,				past_key_values=a_ )["last_hidden_state"]

									# select random slice
									lowercase       		=		ids_tensor((1,)				,				output_from_past.shape[-1] ).item()
									lowercase       		=		output_from_no_past[:, -1, random_slice_idx].detach()
									lowercase       		=		output_from_past[:, 0, random_slice_idx].detach()

									# test that outputs are equal for slice
									self.parent.assertTrue(torch.allclose(a_				,				a_				,				atol=1E-3 ) )



						def 							__a		(						self					:   List[Any]				,				__lowerCamelCase					:   List[Any]				,				__lowerCamelCase					:   List[str]				,				)				->		Any:


									'''simple docstring'''

									lowercase       		=		UMTaModel(config=a_ ).to(a_ ).half().eval()
									lowercase       		=		model(**a_ )["last_hidden_state"]
									self.parent.assertFalse(torch.isnan(a_ ).any().item() )






@require_torch
class   __lowercase       (     _A							,  _A							,  _A							,  unittest.TestCase     ):
						lowercase       			=  (
						    (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
						)
						lowercase       			=  (UMTaForConditionalGeneration,) if is_torch_available() else ()
						lowercase       			=  (
						    {
						        'conversational': UMTaForConditionalGeneration,
						        'feature-extraction': UMTaModel,
						        'summarization': UMTaForConditionalGeneration,
						        'text2text-generation': UMTaForConditionalGeneration,
						        'translation': UMTaForConditionalGeneration,
						        'question-answering': UMTaForQuestionAnswering,
						    }
						    if is_torch_available()
						    else {}
						)
						lowercase       			=  True
						lowercase       			=  False
						lowercase       			=  False
						lowercase       			=  True
						lowercase       			=  True
						# The small UMT5 model needs higher percentages for CPU/MP tests
						lowercase       			=  [0.8, 0.9]

						def 							__a		(						self					:   List[Any] )				->		Tuple:


									'''simple docstring'''

									lowercase       		=		UMTaModelTester(self )

						@unittest.skip('''Test has a segmentation fault on torch 1.8.0''' )
						def 							__a		(						self					:   str )				->		int:


									'''simple docstring'''

									lowercase       		=		self.model_tester.prepare_config_and_inputs()
									lowercase       		=		UMTaModel(config_and_inputs[0] ).to(a_ )
									with tempfile.TemporaryDirectory() as tmpdirname:
												torch.onnx.export(
												    a_				,				(config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])				,				f'{tmpdirname}/t5_test.onnx'				,				export_params=a_				,				opset_version=9				,				input_names=['''input_ids''', '''decoder_input_ids''']				,				)

						@unittest.skipIf(torch_device == '''cpu'''				,				'''Cant do half precision''' )
						def 							__a		(						self					:   Optional[int] )				->		List[str]:


									'''simple docstring'''

									lowercase       		=		self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model_fpaa_forward(*a_ )

						def 							__a		(						self					:   int )				->		List[str]:


									'''simple docstring'''

									lowercase       		=		["encoder_attentions", "decoder_attentions", "cross_attentions"]
									lowercase       		=		self.model_tester.prepare_config_and_inputs()
									lowercase       		=		config_and_inputs[0]
									lowercase       		=		UMTaForConditionalGeneration(a_ ).eval()
									model.to(a_ )

									lowercase       		=		{
									    "head_mask": torch.zeros(config.num_layers				,				config.num_heads				,				device=a_ ),
									    "decoder_head_mask": torch.zeros(config.num_decoder_layers				,				config.num_heads				,				device=a_ ),
									    "cross_attn_head_mask": torch.zeros(config.num_decoder_layers				,				config.num_heads				,				device=a_ ),
									}

									for attn_name, (name, mask) in zip(a_				,				head_masking.items() ):
												lowercase       		=		{name: mask}
												# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
												if name == "head_mask":
															lowercase       		=		torch.ones(
															    config.num_decoder_layers				,				config.num_heads				,				device=a_ )

												lowercase       		=		model.generate(
												    config_and_inputs[1]['''input_ids''']				,				num_beams=1				,				max_length=3				,				output_attentions=a_				,				return_dict_in_generate=a_				,				**a_				,				)
												# We check the state of decoder_attentions and cross_attentions just from the last step
												lowercase       		=		out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
												self.assertEqual(sum([w.sum().item() for w in attn_weights] )				,				0.0 )



						@unittest.skip('''Does not work on the tiny model as we keep hitting edge cases.''' )
						def 							__a		(						self					:   Optional[int] )				->		int:


									'''simple docstring'''

									pass






@require_torch
@require_sentencepiece
@require_tokenizers
class   __lowercase       (     unittest.TestCase     ):

						@slow
						@unittest.skip(
						    '''Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged''' )
						def 							__a		(						self					:   Optional[int] )				->		Any:


									'''simple docstring'''

									lowercase       		=		UMTaForConditionalGeneration.from_pretrained('''google/umt5-small'''				,				return_dict=a_ ).to(a_ )
									lowercase       		=		AutoTokenizer.from_pretrained('''google/umt5-small'''				,				use_fast=a_				,				legacy=a_ )
									lowercase       		=		[
									    "Bonjour monsieur <extra_id_0> bien <extra_id_1>.",
									    "No se como puedo <extra_id_0>.",
									    "This is the reason why we <extra_id_0> them.",
									    "The <extra_id_0> walks in <extra_id_1>, seats",
									    "A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.",
									]
									lowercase       		=		tokenizer(a_				,				return_tensors='''pt'''				,				padding=a_ ).input_ids
									# fmt: off
									lowercase       		=		torch.tensor(
									    [
									        [ 3_85_30, 21_07_03, 25_62_99, 14_10, 25_62_98, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
									        [   8_26, 3_21, 6_71, 2_59_22, 25_62_99, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
									        [  14_60, 3_39, 3_12, 1_90_14, 1_06_20, 7_58, 25_62_99, 23_55,2_74, 1, 0, 0, 0, 0, 0, 0,0, 0],
									        [   5_17, 25_62_99, 1_48_69, 2_81, 3_01, 25_62_98, 2_75, 11_99_83,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
									        [   3_20, 25_62_99, 1_48_69, 2_81, 22_34, 2_89, 22_75, 3_33,6_13_91, 2_89, 25_62_98, 5_43, 25_62_97, 16_87_14, 3_29, 25_62_96,2_74, 1],
									    ] )
									# fmt: on
									torch.testing.assert_allclose(a_				,				a_ )

									lowercase       		=		model.generate(input_ids.to(a_ ) )
									lowercase       		=		[
									    "<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>",
									    "<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
									    "<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
									    "<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
									    "<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
									]
									lowercase       		=		tokenizer.batch_decode(a_ )
									self.assertEqual(a_				,				a_ )












'''simple docstring'''






import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional

import datasets
import evaluate
import numpy as np
from datasets import load_dataset

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version


# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("""4.31.0""")

require_version("""datasets>=1.8.0""", """To fix: pip install -r examples/pytorch/text-classification/requirements.txt""")

lowerCAmelCase									=   logging.getLogger(__name__)
@dataclass
class 		lowerCamelCase	:
	snake_case_   = field(
	    default=128   , metadata={
	        "help": (
	            "The maximum total input sequence length after tokenization. Sequences longer "
	            "than this will be truncated, sequences shorter will be padded."
	        )
	    }   , )
	snake_case_   = field(
	    default=_A   , metadata={"help": "Overwrite the cached preprocessed datasets or not."}							)
	snake_case_   = field(
	    default=_A   , metadata={
	        "help": (
	            "Whether to pad all samples to `max_seq_length`. "
	            "If False, will pad the samples dynamically when batching to the maximum length in the batch."
	        )
	    }   , )
	snake_case_   = field(
	    default=_A   , metadata={
	        "help": (
	            "For debugging purposes or quicker training, truncate the number of training examples to this "
	            "value if set."
	        )
	    }   , )
	snake_case_   = field(
	    default=_A   , metadata={
	        "help": (
	            "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
	            "value if set."
	        )
	    }   , )
	snake_case_   = field(
	    default=_A   , metadata={
	        "help": (
	            "For debugging purposes or quicker training, truncate the number of prediction examples to this "
	            "value if set."
	        )
	    }   , )






@dataclass
class 		lowerCamelCase	:
	snake_case_   = field(
	    default=_A   , metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}							)
	snake_case_   = field(
	    default=_A   , metadata={"help": "Evaluation language. Also train language if `train_language` is set to None."}							)
	snake_case_   = field(
	    default=_A   , metadata={"help": "Train language if it is different from the evaluation language."}							)
	snake_case_   = field(
	    default=_A   , metadata={"help": "Pretrained config name or path if not the same as model_name"}							)
	snake_case_   = field(
	    default=_A   , metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}							)
	snake_case_   = field(
	    default=_A   , metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}   , )
	snake_case_   = field(
	    default=_A   , metadata={"help": "arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()"}   , )
	snake_case_   = field(
	    default=_A   , metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."}   , )
	snake_case_   = field(
	    default="main"   , metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}   , )
	snake_case_   = field(
	    default=_A   , metadata={
	        "help": (
	            "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
	            "with private models)."
	        )
	    }   , )
	snake_case_   = field(
	    default=_A   , metadata={"help": "Will enable to load a pretrained model whose head dimensions are different."}   , )






def   __A    (				):
		# See all possible arguments in src/transformers/training_args.py
		# or by passing the --help flag to this script.
		# We now keep distinct sets of args, for a cleaner separation of concerns.

		lowerCAmelCase  :     List[str]       						= HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)						)
		lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase  :     Tuple       						= parser.parse_args_into_dataclasses()

		# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
		# information sent is the one passed as arguments along with your Python/PyTorch versions.
		send_example_telemetry("run_xnli"   ,a_						)

		# Setup logging
		logging.basicConfig(
		    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"   ,datefmt="%m/%d/%Y %H:%M:%S"   ,handlers=[logging.StreamHandler(sys.stdout						)]   ,)

		if training_args.should_log:
				# The default of training_args.log_level is passive, so we set log level at info here to have that default.
				transformers.utils.logging.set_verbosity_info()

		lowerCAmelCase  :     str       						= training_args.get_process_log_level()
		logger.setLevel(a_						)
		datasets.utils.logging.set_verbosity(a_						)
		transformers.utils.logging.set_verbosity(a_						)
		transformers.utils.logging.enable_default_handler()
		transformers.utils.logging.enable_explicit_format()

		# Log on each process the small summary:
		logger.warning(
		    f'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
		    + f'''distributed training: {bool(training_args.local_rank != -1						)}, 16-bits training: {training_args.fpaa}'''						)
		logger.info(f'''Training/evaluation parameters {training_args}'''						)

		# Detecting last checkpoint.
		lowerCAmelCase  :     Union[str, Any]       						= None
		if os.path.isdir(training_args.output_dir						) and training_args.do_train and not training_args.overwrite_output_dir:
				lowerCAmelCase  :     Dict       						= get_last_checkpoint(training_args.output_dir						)
				if last_checkpoint is None and len(os.listdir(training_args.output_dir						)						) > 0:
						raise ValueError(
						    f'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
						    "Use --overwrite_output_dir to overcome."						)
				elif last_checkpoint is not None:
						logger.info(
						    f'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
						    "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."						)

    # Set seed before initializing model.
		set_seed(training_args.seed						)

		# In distributed training, the load_dataset function guarantees that only one local process can concurrently
		# download the dataset.
		# Downloading and loading xnli dataset from the hub.
		if training_args.do_train:
				if model_args.train_language is None:
						lowerCAmelCase  :     Any       						= load_dataset(
						    "xnli"   ,model_args.language   ,split="train"   ,cache_dir=model_args.cache_dir   ,use_auth_token=True if model_args.use_auth_token else None   ,)
				else:
						lowerCAmelCase  :     Union[str, Any]       						= load_dataset(
						    "xnli"   ,model_args.train_language   ,split="train"   ,cache_dir=model_args.cache_dir   ,use_auth_token=True if model_args.use_auth_token else None   ,)
				lowerCAmelCase  :     int       						= train_dataset.features["label"].names

		if training_args.do_eval:
				lowerCAmelCase  :     Any       						= load_dataset(
				    "xnli"   ,model_args.language   ,split="validation"   ,cache_dir=model_args.cache_dir   ,use_auth_token=True if model_args.use_auth_token else None   ,)
				lowerCAmelCase  :     Dict       						= eval_dataset.features["label"].names

		if training_args.do_predict:
				lowerCAmelCase  :     Optional[Any]       						= load_dataset(
				    "xnli"   ,model_args.language   ,split="test"   ,cache_dir=model_args.cache_dir   ,use_auth_token=True if model_args.use_auth_token else None   ,)
				lowerCAmelCase  :     List[Any]       						= predict_dataset.features["label"].names

		# Labels
		lowerCAmelCase  :     Tuple       						= len(a_						)

		# Load pretrained model and tokenizer
		# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
		# download model & vocab.
		lowerCAmelCase  :     List[str]       						= AutoConfig.from_pretrained(
		    model_args.config_name if model_args.config_name else model_args.model_name_or_path   ,num_labels=a_   ,idalabel={str(a_						): label for i, label in enumerate(a_						)}   ,labelaid={label: i for i, label in enumerate(a_						)}   ,finetuning_task="xnli"   ,cache_dir=model_args.cache_dir   ,revision=model_args.model_revision   ,use_auth_token=True if model_args.use_auth_token else None   ,)
		lowerCAmelCase  :     Optional[Any]       						= AutoTokenizer.from_pretrained(
		    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path   ,do_lower_case=model_args.do_lower_case   ,cache_dir=model_args.cache_dir   ,use_fast=model_args.use_fast_tokenizer   ,revision=model_args.model_revision   ,use_auth_token=True if model_args.use_auth_token else None   ,)
		lowerCAmelCase  :     List[Any]       						= AutoModelForSequenceClassification.from_pretrained(
		    model_args.model_name_or_path   ,from_tf=bool(".ckpt" in model_args.model_name_or_path						)   ,config=a_   ,cache_dir=model_args.cache_dir   ,revision=model_args.model_revision   ,use_auth_token=True if model_args.use_auth_token else None   ,ignore_mismatched_sizes=model_args.ignore_mismatched_sizes   ,)

		# Preprocessing the datasets
		# Padding strategy
		if data_args.pad_to_max_length:
				lowerCAmelCase  :     int       						= "max_length"
		else:
				# We will pad later, dynamically at batch creation, to the max sequence length in each batch
				lowerCAmelCase  :     Union[str, Any]       						= False

		def preprocess_function(a_    :	Dict						):
				# Tokenize the texts
				return tokenizer(
				    examples["premise"]   ,examples["hypothesis"]   ,padding=a_   ,max_length=data_args.max_seq_length   ,truncation=a_   ,)

		if training_args.do_train:
				if data_args.max_train_samples is not None:
						lowerCAmelCase  :     Tuple       						= min(len(a_						)   ,data_args.max_train_samples						)
						lowerCAmelCase  :     int       						= train_dataset.select(range(a_						)						)
				with training_args.main_process_first(desc="train dataset map pre-processing"						):
						lowerCAmelCase  :     Optional[int]       						= train_dataset.map(
						    a_   ,batched=a_   ,load_from_cache_file=not data_args.overwrite_cache   ,desc="Running tokenizer on train dataset"   ,)
				# Log a few random samples from the training set:
				for index in random.sample(range(len(a_						)						)   ,3						):
						logger.info(f'''Sample {index} of the training set: {train_dataset[index]}.'''						)

		if training_args.do_eval:
				if data_args.max_eval_samples is not None:
						lowerCAmelCase  :     int       						= min(len(a_						)   ,data_args.max_eval_samples						)
						lowerCAmelCase  :     Optional[Any]       						= eval_dataset.select(range(a_						)						)
				with training_args.main_process_first(desc="validation dataset map pre-processing"						):
						lowerCAmelCase  :     Optional[Any]       						= eval_dataset.map(
						    a_   ,batched=a_   ,load_from_cache_file=not data_args.overwrite_cache   ,desc="Running tokenizer on validation dataset"   ,)

		if training_args.do_predict:
				if data_args.max_predict_samples is not None:
						lowerCAmelCase  :     Optional[Any]       						= min(len(a_						)   ,data_args.max_predict_samples						)
						lowerCAmelCase  :     Optional[Any]       						= predict_dataset.select(range(a_						)						)
				with training_args.main_process_first(desc="prediction dataset map pre-processing"						):
						lowerCAmelCase  :     Tuple       						= predict_dataset.map(
						    a_   ,batched=a_   ,load_from_cache_file=not data_args.overwrite_cache   ,desc="Running tokenizer on prediction dataset"   ,)

    # Get the metric function
		lowerCAmelCase  :     str       						= evaluate.load("xnli"						)

		# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
		# predictions and label_ids field) and has to return a dictionary string to float.
		def compute_metrics(a_    :	EvalPrediction						):
				lowerCAmelCase  :     Union[str, Any]       						= p.predictions[0] if isinstance(p.predictions   ,a_						) else p.predictions
				lowerCAmelCase  :     Any       						= np.argmax(a_   ,axis=1						)
				return metric.compute(predictions=a_   ,references=p.label_ids						)

		# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
		if data_args.pad_to_max_length:
				lowerCAmelCase  :     Optional[Any]       						= default_data_collator
		elif training_args.fpaa:
				lowerCAmelCase  :     Union[str, Any]       						= DataCollatorWithPadding(a_   ,pad_to_multiple_of=8						)
		else:
				lowerCAmelCase  :     str       						= None

		# Initialize our Trainer
		lowerCAmelCase  :     Tuple       						= Trainer(
		    model=a_   ,args=a_   ,train_dataset=train_dataset if training_args.do_train else None   ,eval_dataset=eval_dataset if training_args.do_eval else None   ,compute_metrics=a_   ,tokenizer=a_   ,data_collator=a_   ,)

		# Training
		if training_args.do_train:
				lowerCAmelCase  :     str       						= None
				if training_args.resume_from_checkpoint is not None:
						lowerCAmelCase  :     Any       						= training_args.resume_from_checkpoint
				elif last_checkpoint is not None:
						lowerCAmelCase  :     List[str]       						= last_checkpoint
				lowerCAmelCase  :     Optional[int]       						= trainer.train(resume_from_checkpoint=a_						)
				lowerCAmelCase  :     List[Any]       						= train_result.metrics
				lowerCAmelCase  :     Optional[Any]       						= (
				    data_args.max_train_samples if data_args.max_train_samples is not None else len(a_						)
				)
				lowerCAmelCase  :     Any       						= min(a_   ,len(a_						)						)

				trainer.save_model()  # Saves the tokenizer too for easy upload

				trainer.log_metrics("train"   ,a_						)
				trainer.save_metrics("train"   ,a_						)
				trainer.save_state()

		# Evaluation
		if training_args.do_eval:
				logger.info("*** Evaluate ***"						)
				lowerCAmelCase  :     Optional[int]       						= trainer.evaluate(eval_dataset=a_						)

				lowerCAmelCase  :     Optional[int]       						= data_args.max_eval_samples if data_args.max_eval_samples is not None else len(a_						)
				lowerCAmelCase  :     Any       						= min(a_   ,len(a_						)						)

				trainer.log_metrics("eval"   ,a_						)
				trainer.save_metrics("eval"   ,a_						)

		# Prediction
		if training_args.do_predict:
				logger.info("*** Predict ***"						)
				lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase  :     Optional[Any]       						= trainer.predict(a_   ,metric_key_prefix="predict"						)

				lowerCAmelCase  :     List[Any]       						= (
				    data_args.max_predict_samples if data_args.max_predict_samples is not None else len(a_						)
				)
				lowerCAmelCase  :     Any       						= min(a_   ,len(a_						)						)

				trainer.log_metrics("predict"   ,a_						)
				trainer.save_metrics("predict"   ,a_						)

				lowerCAmelCase  :     Optional[int]       						= np.argmax(a_   ,axis=1						)
				lowerCAmelCase  :     Any       						= os.path.join(training_args.output_dir   ,"predictions.txt"						)
				if trainer.is_world_process_zero():
						with open(a_   ,"w"						) as writer:
								writer.write("index\tprediction\n"						)
								for index, item in enumerate(a_						):
										lowerCAmelCase  :     str       						= label_list[item]
										writer.write(f'''{index}\t{item}\n'''						)


if __name__ == "__main__":
					main()



